Multiscale Forecasting Models / by Lida Mercedes Barba Maggi.
2018
Q334-342
Linked e-resources
Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Multiscale Forecasting Models / by Lida Mercedes Barba Maggi.
ISBN
9783319949925
3319949926
9783319949925
3319949918
9783319949918
3319949926
9783319949925
3319949918
9783319949918
Published
Cham : Springer International Publishing : Imprint: Springer, 2018.
Language
English
Description
1 online resource (XXIV, 124 pages) : illustrations.
Item Number
10.1007/978-3-319-94992-5. doi
Call Number
Q334-342
Dewey Decimal Classification
006.3
Summary
This book presents two new decomposition methods to decompose a time series in intrinsic components of low and high frequencies. The methods are based on Singular Value Decomposition (SVD) of a Hankel matrix (HSVD). The proposed decomposition is used to improve the accuracy of linear and nonlinear auto-regressive models. Linear Auto-regressive models (AR, ARMA and ARIMA) and Auto-regressive Neural Networks (ANNs) have been found insufficient because of the highly complicated nature of some time series. Hybrid models are a recent solution to deal with non-stationary processes which combine pre-processing techniques with conventional forecasters, some pre-processing techniques broadly implemented are Singular Spectrum Analysis (SSA) and Stationary Wavelet Transform (SWT). Although the flexibility of SSA and SWT allows their usage in a wide range of forecast problems, there is a lack of standard methods to select their parameters. The proposed decomposition HSVD and Multilevel SVD are described in detail through time series coming from the transport and fishery sectors. Further, for comparison purposes, it is evaluated the forecast accuracy reached by SSA and SWT, both jointly with AR-based models and ANNs.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Available in Other Form
Print version: 9783319949918
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
All Resources